EP1533628A1 - Methode zur Korrelation und Nummerierung von Zielspuren aus mehreren Quellen - Google Patents

Methode zur Korrelation und Nummerierung von Zielspuren aus mehreren Quellen Download PDF

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Publication number
EP1533628A1
EP1533628A1 EP03445132A EP03445132A EP1533628A1 EP 1533628 A1 EP1533628 A1 EP 1533628A1 EP 03445132 A EP03445132 A EP 03445132A EP 03445132 A EP03445132 A EP 03445132A EP 1533628 A1 EP1533628 A1 EP 1533628A1
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Prior art keywords
target
track
tracks
hypotheses
probabilities
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EP03445132A
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French (fr)
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EP1533628B1 (de
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Niclas Bergman
Egils Sviestins
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Saab AB
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Saab AB
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional [2D] tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional [2D] tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726—Multiple target tracking

Definitions

  • the present invention relates to target tracking, in particular the correlation of tracks maintained by different trackers, into a single set of tracks, and assigning a track number to each track in the correlated set.
  • Target tracking is the process by which the location, motion, and possibly identity, of objects like aircraft, ships, and ground vehicles, are established and presented to users, for example situated at command and control centers.
  • the detection of the targets is accomplished by one or more sensors, where the most widely used sensor for air targets is the radar. Examples of other sensors of interest are infrared sensors, ESM (Electronic Support Measures) which detect radar radiation from the target, and jam strobe detectors. Satellite navigation data may also provide location data for friendly targets.
  • a tracker takes a sequence of measured target locations, and determines which measurements belong to true targets, creates a track for each target, assigns an arbitrary track number, and computes speed, heading, and position (with reduced noise errors) for the target. The track data are subsequently updated as more measurements enter the tracker.
  • a tracker may use measurements from a single sensor or from multiple sensors (multisensor tracking).
  • the present invention does not deal with the actual tracking process.
  • the purpose is to combine the tracks produced by several trackers into a situation picture, and assign a global track number to the combined tracks.
  • An object of the invention is to provide a method for correlating tracks from different track sources and at the same time performing track number assignment.
  • the object of the invention is to provide a method for correlating tracks from different track sources and assign track numbers in a way that for performance reasons is based on Bayesian hypothesis testing of a large number of hypotheses.
  • Yet another object of the invention is to provide a method for correlating tracks from different track sources and assign track numbers in a way that is suitable for recurrent evaluation and correction of correlations and track number assignments.
  • Still another object of the invention is to provide a method that can serve as the basis for distributed correlation and track number assignment, i.e. where the correlation takes place at several nodes in a network to form a common situation picture.
  • the cornerstone of the invention is that the hypothesis testing encompasses the correlation between tracks and (unknown) targets, instead of the traditional track-to-track correlation. If the algorithm assigns the same target to two different tracks, it implies, as a side effect, that the two tracks are correlated.
  • a large number of track-to-target correlation hypotheses are generated, and probabilities are assigned to all or a subset of these hypotheses. Then the sensor track positions are compared and used for updating the probabilities of the hypotheses.
  • the algorithm produces suggestions for recorrelation (i.e. changing the track-to-target assignments) for operator approval or for automatic execution.
  • the present invention uses track data as input.
  • the tracks are produced by trackers 12 which can be located remotely, often at the sensors 11, or at a different command & control center, or at the same site or even the same computer where the track correlator 13 resides.
  • Track data usually comprise
  • the tracks 14 are sent to the track correlator 13 at time intervals which depend on tracker design.
  • the task of the correlator is to produce system tracks 15 based on the incoming local tracks, and make sure that if two local tracks 14 represent the same target, they both contribute to the same system track 15.
  • the system track may be formed by fusing (for example averaging) the corresponding local tracks, or they may simply consist of the best of the local tracks.
  • the present invention does not include this functionality. It produces lists of system track numbers and the corresponding local track numbers, and it is then up to the user to decide whether the local tracks should be fused or selected.
  • Each correlation cycle starts with a trigger signal 101, typically after a certain time, although one could also consider various scenario dependent criteria. If there are no tracks to correlate 102, then the process enters a wait state, otherwise the next step is clustering, 103. The number of hypotheses can be extremely large. Therefore the set of tracks is divided into clusters that can be processed one at a time. The division is done in such a way that it is very unlikely that a local track in one cluster could be related to a local track in another cluster.
  • the criteria for clustering should be based on track positions, and earlier target number assignments.
  • the following step is updating 105, where the various hypotheses are compared with new incoming data, i.e., tracks, to compute new probabilities.
  • new incoming data i.e., tracks
  • next cluster can be processed, and when the clusters are exhausted, the process waits for the next trigger.
  • Fig 3 gives an example on how target numbers relate to track numbers.
  • Source 1 provides local tracks L11, L12 and L13
  • source 2 has tracks L21 and L22.
  • the algorithm may have assigned target T1 to L11, T2 to L13 and L21, T3 to L22 and T4 to L12.
  • target 2 is tracked by both sources, while the other targets are tracked by a single source.
  • the second track from the first source represents target 1.
  • Target 2 is tracked by all three sources.
  • the first track of the third source does not go with any of the hitherto handled targets, but instead represents either a false track, or a new real target.
  • One interpretation is that there are in fact three targets, but due to poor sensor resolution, no single sensor has yet detected all three.
  • Another interpretation is that target 1 was tracked by both source 1 and source 3, but due to difficulties in sensor coverage or a very sudden strong maneuver, L12 and L31 have diverged, and are no longer compatible. It is up to the correlator, along with information from the trackers, to determine whether it is L12 or L31 that has been disconnected. Of course there are many other correlation possibilities; the table shows just one them.
  • the local tracks from sensor 1 can be distributed in six ways among T1, T2 and T3, L21 can be put in three places, and L31 and L32 in six combinations. This makes a total of 108 hypotheses.
  • T column will be empty. According to such hypotheses, the target in question does not exist. Perhaps it was a false target, and the corresponding local track should be associated with another, true, target.
  • the probability mass tends to go from the most likely hypothesis to some other possible hypothesis, as there is always some risk that previous correct track-to-target assignments do not hold because of tracking errors. Expressing these probability changes is however very difficult, and in reality more or less heuristic schemes have to be used. These should express the fact that in difficult conditions - dense target situations, inadequate sensor coverage, etc. - there is a larger probability for tracking errors than in perfectly unambiguous cases.
  • the updating step is normally (but not necessarily) based on Bayes rule, which gives where is the probability of hypothesis H n after the prediction step, and p ( z
  • the constant of proportionality can be determined by making sure that the sum over the hypotheses is 1. can then be used as the starting point for the next prediction leading to
  • Bayes rule it is important that the sequence of measurements is independent. One may thus not, in effect, feed in the same information z over again, as that will produce erroneous probabilities.
  • Bayes rule There are ways of handling dependent measurements in a Bayesian framework, but they are complicated and require accurate knowledge on the nature of dependencies. We simply avoid this problem by not updating too frequently, that is, tracks must have moved a long way compared to the positional uncertainties involved.
  • the positions are never exact, instead they are more appropriately described by probability densities f jr ( x , x r ) around the true positions.
  • the probability of finding the track within a small volume dz around z jr is then f jr ( z jr , x r ) dz .
  • the probability of finding all the tracks corresponding to x r at their given positions is
  • Each sensor may have a limited capability to see a certain target.
  • the sensors may have different coverage areas in space or electromagnetic spectrum, and thus it is perfectly possible that a target is not seen by some of the sensors. This should be taken into account when computing the probabilities for the hypotheses, otherwise the algorithm may force two independent tracks to represent the same target.
  • the probability that a target is seen by a source can be expressed by a visibility factor 0 ⁇ K jr ⁇ 1. Likewise, the probability that a target is not seen by a source will be 1- K kr .
  • T1 T2 T3 L12 L11 L21 L32 L31 we should produce visibility factors according to T1 T2 T3 K11 K12 1-K13 1-K21 K22 1-K23 1-K31 K32 K33
  • both are T 2 : T1 T2 L11 L21 and the unrealistic H 4 , where both target numbers are replaced: T1 T2 L11 L21
  • a more sophisticated version of the simplified representation could consist in storing more than one target number and its probability in each track. Although this could be very helpful in allowing secondary hypotheses to grow, it adds significantly to the complexity of the algorithm.
  • the number of hypotheses to be evaluated can be extremely large even for small clusters. For example, with three track sources, each with three tracks, and with five possible target numbers, the number of hypotheses is 216000. It would be impractical to compute the updated probabilities for all hypotheses. A suggestion for a simplified scheme, to be invoked when the cluster is large, is not to compute the probabilities for all hypotheses, but only those that seem to be realistic.
  • One or both of the following methods can be employed:
  • FIG. 5 An application where this invention may be very useful is distributed correlation, please refer to Figure 5.
  • This case has several correlators 16, 17 running at different sites, each correlator being connected to one or more trackers 19, 20, 21.
  • the correlators are communicating over some kind of network 18, with the aim of forming a common situation picture.
  • the global track numbers i.e. target numbers
  • the most likely target number for each track may conveniently serve as the global track number.
  • both correlators should arrive at the same conclusion as to which track should change the number.
  • additional rules may have to be included concerning e.g. track quality or track age, to control which site should do the renumbering.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)
EP20030445132 2003-11-19 2003-11-19 Methode zur Korrelation und Nummerierung von Zielspuren aus mehreren Quellen Expired - Lifetime EP1533628B1 (de)

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Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008133741A3 (en) * 2006-12-20 2008-12-24 Raytheon Co Multiple sensor processing
CN102162847A (zh) * 2011-01-14 2011-08-24 中国人民解放军理工大学 一种基于奇异值分解的高效航迹相关方法
US20130006580A1 (en) * 2010-03-22 2013-01-03 Bae Systems Plc Generating an indication of a probability of a hypothesis being correct based on a set of observations
US20140191899A1 (en) * 2012-06-14 2014-07-10 Catherine Pickle Systems and methods for tracking targets by a through-the-wall radar using multiple hypothesis tracking
EP2980605A1 (de) * 2014-07-31 2016-02-03 Honeywell International Inc. Aktualisierung der intensitäten in einem phd-filter auf basis einer sensor-track-id
CN105321381A (zh) * 2014-07-31 2016-02-10 霍尼韦尔国际公司 基于传感器轨迹id调整phd过滤器中的强度的权重
WO2016034695A1 (fr) * 2014-09-05 2016-03-10 Thales Procede de gestion de croisements dans le suivi d'objets mobiles et dispositif associe
US10309784B2 (en) 2014-07-31 2019-06-04 Honeywell International Inc. Merging intensities in a PHD filter based on a sensor track ID
CN110109097A (zh) * 2019-06-06 2019-08-09 电子科技大学 一种扫描雷达前视成像方位超分辨方法
JP2020034363A (ja) * 2018-08-29 2020-03-05 沖電気工業株式会社 信号追尾装置、信号追尾システム、信号追尾方法およびプログラム
US10605607B2 (en) 2014-07-31 2020-03-31 Honeywell International Inc. Two step pruning in a PHD filter
US11282158B2 (en) * 2019-09-26 2022-03-22 Robert Bosch Gmbh Method for managing tracklets in a particle filter estimation framework
CN114548312A (zh) * 2022-03-01 2022-05-27 东南大学 基于改进ospa距离指标的航迹关联快速聚类方法
CN115598630A (zh) * 2022-11-24 2023-01-13 中国船舶重工集团公司第七一五研究所（Cn） 一种基于航迹关联的多假设自动跟踪方法
CN116794646A (zh) * 2023-06-19 2023-09-22 哈尔滨工业大学 基于变分贝叶斯推理的混合体制高频雷达目标跟踪方法

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CN104050368B (zh) * 2014-06-09 2017-04-12 中国人民解放军海军航空工程学院 系统误差下基于误差补偿的群航迹精细关联算法

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Cited By (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008133741A3 (en) * 2006-12-20 2008-12-24 Raytheon Co Multiple sensor processing
US7508335B2 (en) 2006-12-20 2009-03-24 Raytheon Company Multiple sensor processing
US20130006580A1 (en) * 2010-03-22 2013-01-03 Bae Systems Plc Generating an indication of a probability of a hypothesis being correct based on a set of observations
CN102162847A (zh) * 2011-01-14 2011-08-24 中国人民解放军理工大学 一种基于奇异值分解的高效航迹相关方法
CN102162847B (zh) * 2011-01-14 2013-02-20 中国人民解放军理工大学 一种基于奇异值分解的高效航迹相关方法
US20140191899A1 (en) * 2012-06-14 2014-07-10 Catherine Pickle Systems and methods for tracking targets by a through-the-wall radar using multiple hypothesis tracking
US8970429B2 (en) * 2012-06-14 2015-03-03 Raytheon Company Systems and methods for tracking targets by a through-the-wall radar using multiple hypothesis tracking
US10605607B2 (en) 2014-07-31 2020-03-31 Honeywell International Inc. Two step pruning in a PHD filter
US11175142B2 (en) 2014-07-31 2021-11-16 Honeywell International Inc. Updating intensities in a PHD filter based on a sensor track ID
CN105321381A (zh) * 2014-07-31 2016-02-10 霍尼韦尔国际公司 基于传感器轨迹id调整phd过滤器中的强度的权重
CN105321380A (zh) * 2014-07-31 2016-02-10 霍尼韦尔国际公司 基于传感器轨迹id更新phd过滤器中的强度
EP2980605A1 (de) * 2014-07-31 2016-02-03 Honeywell International Inc. Aktualisierung der intensitäten in einem phd-filter auf basis einer sensor-track-id
CN105321380B (zh) * 2014-07-31 2020-02-07 霍尼韦尔国际公司 基于传感器轨迹id更新phd过滤器中的强度
US10309784B2 (en) 2014-07-31 2019-06-04 Honeywell International Inc. Merging intensities in a PHD filter based on a sensor track ID
CN105321381B (zh) * 2014-07-31 2020-02-07 霍尼韦尔国际公司 基于传感器轨迹id调整phd过滤器中的强度的权重
US10254394B2 (en) 2014-09-05 2019-04-09 Thales Method for managing crossovers in the tracking of mobile objects, and associated device
FR3025609A1 (fr) * 2014-09-05 2016-03-11 Thales Sa Procede de gestion de croisements dans le suivi d'objets mobiles et dispositif associe
WO2016034695A1 (fr) * 2014-09-05 2016-03-10 Thales Procede de gestion de croisements dans le suivi d'objets mobiles et dispositif associe
JP2020034363A (ja) * 2018-08-29 2020-03-05 沖電気工業株式会社 信号追尾装置、信号追尾システム、信号追尾方法およびプログラム
JP7067373B2 (ja) 2018-08-29 2022-05-16 沖電気工業株式会社 信号追尾装置、信号追尾システム、信号追尾方法およびプログラム
CN110109097A (zh) * 2019-06-06 2019-08-09 电子科技大学 一种扫描雷达前视成像方位超分辨方法
CN110109097B (zh) * 2019-06-06 2021-04-13 电子科技大学 一种扫描雷达前视成像方位超分辨方法
US11282158B2 (en) * 2019-09-26 2022-03-22 Robert Bosch Gmbh Method for managing tracklets in a particle filter estimation framework
CN114548312A (zh) * 2022-03-01 2022-05-27 东南大学 基于改进ospa距离指标的航迹关联快速聚类方法
CN115598630A (zh) * 2022-11-24 2023-01-13 中国船舶重工集团公司第七一五研究所（Cn） 一种基于航迹关联的多假设自动跟踪方法
CN116794646A (zh) * 2023-06-19 2023-09-22 哈尔滨工业大学 基于变分贝叶斯推理的混合体制高频雷达目标跟踪方法
CN116794646B (zh) * 2023-06-19 2024-04-19 哈尔滨工业大学 基于变分贝叶斯推理的混合体制高频雷达目标跟踪方法

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